This is an interesting view on AI, but IMO I don’t really share this view, and think that the evolutionary/memetic aspect of AI is way overplayed, compared to other factors that make AI powerful.
A big reason for that is that there will be higher-level bounds on what exactly is selected for, and in particular one big difference between computer code used on AI and genetic code is that genetic code has way less ability to error-correct than basically all AI code, and it’s in a weird spot of reliability where random mutations are frequent enough to drive evolution, but not so frequent as to cause organisms to outright collapse within seconds or minutes.
Another reason is that effective AI architectures can’t go through simulated evolution, since that would use up too much compute for training to work (We forget that evolution had at a lower bound 10e46 FLOPs to 10e48 FLOPs to get to humans).
A better analogy is within human-lifetime learning.
I basically agree with Steven Byrnes’s case against evolution, and think that evolutionary analogies are very overplayed in the popular press:
The ‘evolutionary pressures’ being discussed by CGP Grey is not the direct gradient descent used to train an individual model. Instead, he is referring to the whole set of incentives we as a society put on AI models. Similar to memes—there is no gradient descent on memes.
(Apologies if you already understood this, but it seems your post and Steven Byrne’s post focus on training of individual models)
Fair enough on that difference between the societial level incentives on AI models and the individual selection incentives on AI models.
My main current response is to say that I think the incentives are fairly weak predictors of the variance in outcomes, compared to non-evolutionary forces at this time.
However, I do think this has interesting consequences for AI governance (since one of the effects is to make societal level incentives become more relevant, compared to non-evolutionary forces.)
This is an interesting view on AI, but IMO I don’t really share this view, and think that the evolutionary/memetic aspect of AI is way overplayed, compared to other factors that make AI powerful.
A big reason for that is that there will be higher-level bounds on what exactly is selected for, and in particular one big difference between computer code used on AI and genetic code is that genetic code has way less ability to error-correct than basically all AI code, and it’s in a weird spot of reliability where random mutations are frequent enough to drive evolution, but not so frequent as to cause organisms to outright collapse within seconds or minutes.
Another reason is that effective AI architectures can’t go through simulated evolution, since that would use up too much compute for training to work (We forget that evolution had at a lower bound 10e46 FLOPs to 10e48 FLOPs to get to humans).
A better analogy is within human-lifetime learning.
I basically agree with Steven Byrnes’s case against evolution, and think that evolutionary analogies are very overplayed in the popular press:
https://www.lesswrong.com/posts/pz7Mxyr7Ac43tWMaC/against-evolution-as-an-analogy-for-how-humans-will-create
The ‘evolutionary pressures’ being discussed by CGP Grey is not the direct gradient descent used to train an individual model. Instead, he is referring to the whole set of incentives we as a society put on AI models. Similar to memes—there is no gradient descent on memes.
(Apologies if you already understood this, but it seems your post and Steven Byrne’s post focus on training of individual models)
Fair enough on that difference between the societial level incentives on AI models and the individual selection incentives on AI models.
My main current response is to say that I think the incentives are fairly weak predictors of the variance in outcomes, compared to non-evolutionary forces at this time.
However, I do think this has interesting consequences for AI governance (since one of the effects is to make societal level incentives become more relevant, compared to non-evolutionary forces.)